The Plausibility of Semantic Properties Generated by a Distributional Model: Evidence from a Visual World Experiment
Diego Frassinelli, Frank Keller · eScholarship (California Digital Library) · 2012
Distributional models of semantics are a popular way of capturing the similarity between words or concepts.More recently, such models have also been used to generate properties associated with a concept; model-generated properties are typically compared against collections of semantic feature norms.In the present paper, we propose a novel way of testing the plausibility of the properties generated by a distributional model using data from a visual world experiment.We show that model-generated properties, when embedded in a sentential context, bias participants' expectations towards a semantically associated target word in real time.This effect is absent in a neutral context that contains no relevant properties.